Most Famous Unsupervised Machine Learning Algorithms
Unsupervised machine learning algorithms to analyze and cluster unlabeled datasets
Greetings.
I am a machine learning engineer based in India, possessing a sustained interest in machine learning since my undergraduate studies. I have completed Stanford University's machine learning course (Andrew Ng) via Coursera, and IBM's machine learning and deep learning curriculum. My current focus is on machine learning and data science projects, aiming to leverage my expertise for impactful, real-world problem-solving.
Unsupervised learning models are utilized for three main tasks such as clustering, association, and dimensionality reduction.
Below we’ll define each learning method and highlight common algorithms and approaches to conduct them effectively.
Most Famous Unsupervised Learning Algorithms
1. Clustering
Clustering is the most famous unsupervised learning algorithm.
Its main purpose is grouping similar data points into the same groups.
Clustering algorithms are used to process raw, unclassified data objects into groups represented by structures or patterns in the information.
Types of Clustering:
Hierarchical Clustering
K - means
Fuzzy Clustering
Mixture Models
2. Principal Component Analysis (PCA)
- Principal component analysis (PCA) is a technique to bring out strong patterns in a dataset by suppressing variations.
PCA is an unsupervised technique used to reduce the dimensionally of high-dimensional datasets while preserving the original structure and relationships between data.
3. Neural Networks
Artificial Neural Networks are normally called Neural Networks (NN).
Neural networks are in fact multi-layer Perceptrons.
Neural nets are most commonly used as a supervised learning algorithm but there are some cases when they are used in an unsupervised learning manner.
- for example Koehnen's Self Organizing Map (KSOM)
